code: def train_epoch(self, epoch, split="train"):
      assert split in ["train"]
      self.logger.info(
          "Start training in split {}, Epoch: {}".format(split, epoch)
      )
      loss_collect = []
      dataloader = self.train_dataloader
      step_all_train = len(dataloader)
  
      for step, data in enumerate(dataloader):
          graph, tgt = data["graph_data"], data["tgt_seq"]
          tgt = tgt.to(self.device)
          graph = graph.to(self.device)
          oov_dict = None
          prob, enc_attn_weights, coverage_vectors = self.model(
              graph, tgt, oov_dict=oov_dict
          )
          loss = self.loss(
              logits=prob,
              label=tgt,
              enc_attn_weights=enc_attn_weights,
              coverage_vectors=coverage_vectors,
          )
  
          # add graph regularization loss if available
          if graph.graph_attributes.get("graph_reg", None) is not None:
              loss = loss + graph.graph_attributes["graph_reg"]
  
          loss_collect.append(loss.item())
          self.global_steps += 1
  
          if step % self.opt["loss_display_step"] == 0 and step != 0:
              self.logger.info(
                  "Epoch {}: [{} / {}] loss: {:.3f}".format(
                      epoch, step, step_all_train, np.mean(loss_collect)
                  )
              )
              loss_collect = []
          self.optimizer.zero_grad()
          loss.backward()
          torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
          self.optimizer.step()
          self.scheduler.step()
          self.writer.add_scalar(
              "train/loss",
              scalar_value=loss.item(),
              global_step=self.global_steps,
          )
          self.writer.add_scalar(
              "train/lr",
              scalar_value=self.scheduler.get_lr()[0],
              global_step=self.global_steps,
          )
  
imports: import os
  import resource
  import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.evaluation import BLEU
  from args import get_args
  from build_model import get_model
  from dataset import IWSLT14Dataset
  from utils import WarmupCosineSchedule, get_log, wordid2str
label_desc: Use image node_id 12 for calling the NMT obj's underlying member method code with example usage: obj.train_epoch(epoch, split) without return types